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Impact of Querying Table Information From information_schema

Percona

A lot of useful information can be retrieved from this schema, for example, table metadata and foreign key relations, but trying to query I_S can induce performance degradation if your server is under heavy load, as shown in the following example test. The same tests have been executed in Percona Server for MySQL 5.7

Cache 103
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How to Assess MySQL Performance

HammerDB

Therefore, before we attempt to measure our database performance, we should know the system or cloud instance to be tested in detail. Benchmarking the target Two of the more popular database benchmarks for MySQL are HammerDB and sysbench. This allows us to know our operating environment and its capability. 4.22 %usr 38.40

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20X Faster Backup Preparation With Percona XtraBackup 8.0.33-28!

Percona

After the “data dictionary” (DD) engine and DD cache are initialized on a server, the Storage Engines can ask for a table definition. Initializing a DD engine and the cache adds complexity and other server dependencies. ibd in the test directory. For example, mysql datadir/test/t1.ibd. ibd2sdi data/test/t1.ibd

Cache 87
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MySQL Key Performance Indicators (KPI) With PMM

Percona

A monitoring tool like Percona Monitoring and Management (PMM) is a popular choice among open source options for effectively monitoring MySQL performance. This includes metrics such as query execution time, the number of queries executed per second, and the utilization of query cache and adaptive hash index.

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The Importance of Selecting the Proper Azure VM Size

SQL Performance

Memory optimized – High memory-to-CPU ratio, relational database servers, medium to large caches, and in-memory analytics. The common trend is to choose a VM based exclusively on vCPU, memory, and storage capacity without benchmarking the current IO and throughput requirements. I put each of these VMs to a test using CrystalDiskMark.

Azure 76
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PostgreSQL Performance Tuning: Optimizing Database Parameters for Maximum Efficiency

Percona

Connection pooling: Minimizing connection overhead and improving response times for frequently accessed data by implementing mechanisms for connection pooling and caching strategies. PostgreSQL performance optimization is an ongoing process involving monitoring, benchmarking, and adjustments to maintain high-performing PostgreSQL databases.

Tuning 52
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Tuning PostgreSQL Database Parameters to Optimize Performance

Percona

This parameter sets how much dedicated memory will be used by PostgreSQL for cache. If your working set of data can easily fit into your RAM, then you might want to increase the shared_buffer value to contain your entire database, so that the whole working set of data can reside in cache. wal_buffers. effective_cache_size.

Tuning 56